Like other neural style transfer algorithms, NNST's pipeline relies on a pre-trained feature extractor \(\Phi (x)\) , where \(x\)  is an RGB image. Similar to STROTSS, \(\Phi (x)\)  extracts the hypercolumns [1]}, [2]} formed from the activations produced for convolutional layers in the first four blocks of pre-trained VGG16 [3]} when \(x\)  is passed in. We use bilinear interpolation on activations from all layers to give them spatial resolution equal to one quarter of the original image. For an image with height H, and width W, this yields an image representation \(\Phi (x) \in \mathbb {R}^{\frac{H}{4} \times \frac{W}{4} \times 2688}\) . Generally we consider style to be rotation invariant, and to reflect this we extract features from the style image rotated at \(0^{\circ }, 90^{\circ },180^{\circ }\)  and \(270^{\circ }\)  in all experiments.
